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CRII: CHS: RUI: Computational models of humans for studying and improving Human-AI interaction

CRII：CHS：RUI：用于研究和改善人机交互的人类计算模型

基本信息

批准号：
2218226
负责人：
Christopher Dancy
金额：
$ 17.41万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-15 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218226&HistoricalAwards=false
关键词：
CRII CHS RUI Computational models

项目摘要

Understanding the interactions between humans and systems utilizing artificial intelligence (AI) requires an understanding of how human physiological changes impact memory and other mental processes. To realize the beneficial societal outcomes on, for example, interactions between humans and intelligent robots, it is important to develop simulations to test a variety of situations where memory under arousal states will function. In this project, the investigator articulates a research plan to use a simulation of the human mind and body to understand effects of physiological arousal on human memory and cognition and the consequences for human interaction with AI agents. Human-subject studies will be used to introduce stimuli to induce human arousal by using selected stressors and collect physiological and behavioral data during tasks that require cooperation between humans and AI agents. Societal benefits include an architecture to simulate a variety of human-AI interactions under various levels of arousal and stress. This architecture can be used to explore new ways to co-team humans with AI agents and set expectations for positive and/or negative behaviors that might occur in such collaborations. Undergraduate students at Bucknell University will be heavily involved as research assistants on this project. The investigator plans to develop simulations and elicit arousal states in human operators performing collaborative tasks with agents enabled by artificial intelligence (AI) to understand human-AI interaction. The goal is to better understand, through simulation, how algorithms for intelligent agents can be advanced and expanded to respond to human variations in behavior and memory processes under different levels of arousal, including levels that mimic stress. The objectives include developing simulations to examine contexts and tasks; understanding how environmental stimuli affect interactions between humans and intelligent agents; and determining how to advance algorithms to optimize human-AI cooperation and avoid maladaptive interactions. The investigator has already extended Adaptive Control of Thought-Rational (ACT-R) architecture to account for physiological influences on declarative and procedural memory. Physio-cognitive agents will be developed based on Bayesian and reinforcement learning to acquire knowledge of their environment. Human-AI task simulations will be implemented in a virtual environment to understand how arousal mediates intelligent behavior and how interaction with external environments that include AI agents may change performance, including through maladaptive behavior. The project seeks to discover computationally-enabled processes and contexts to amplify human capabilities. The resulting revised models and open-source code will be made available to the public for further explorations in human-AI interaction. The university is an undergraduate institution allowing significant participation of undergraduates in research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

了解人类与利用人工智能 (AI) 的系统之间的交互需要了解人类生理变化如何影响记忆和其他心理过程。为了实现有益的社会成果，例如人类与智能机器人之间的互动，重要的是开发模拟来测试唤醒状态下记忆发挥作用的各种情况。在这个项目中，研究人员制定了一项研究计划，利用人类思维和身体的模拟来了解生理唤醒对人类记忆和认知的影响以及人类与人工智能代理交互的后果。人类受试者研究将用于引入刺激，通过使用选定的压力源来诱导人类觉醒，并在需要人类和人工智能代理之间合作的任务期间收集生理和行为数据。社会效益包括模拟不同程度的唤醒和压力下的各种人类与人工智能交互的架构。该架构可用于探索人类与人工智能代理合作的新方法，并为此类合作中可能发生的积极和/或消极行为设定期望。巴克内尔大学的本科生将作为研究助理大量参与该项目。研究人员计划开发模拟，并在人类操作员与人工智能 (AI) 支持的代理执行协作任务时引发唤醒状态，以了解人机交互。目标是通过模拟更好地理解如何改进和扩展智能代理的算法，以响应不同唤醒水平（包括模拟压力水平）下人类行为和记忆过程的变化。目标包括开发模拟来检查环境和任务；了解环境刺激如何影响人类和智能体之间的互动；并确定如何改进算法以优化人类与人工智能的合作并避免适应不良的交互。研究人员已经扩展了思维理性自适应控制（ACT-R）架构，以解释对陈述性记忆和程序性记忆的生理影响。物理认知代理将基于贝叶斯和强化学习来开发，以获取其环境知识。人类-人工智能任务模拟将在虚拟环境中实施，以了解唤醒如何调节智能行为，以及与包括人工智能代理在内的外部环境的交互如何可能改变性能，包括通过适应不良行为。该项目旨在发现计算支持的流程和环境，以增强人类的能力。由此产生的修订模型和开源代码将向公众开放，以进一步探索人机交互。该大学是一所本科院校，允许本科生大量参与研究。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。